With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning ...With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.展开更多
Growth can be defined as an increment in biomass or an increment in weight or height of the organs of the plant influenced by physiological processes.Many of these processes have their limits genetically determined,bu...Growth can be defined as an increment in biomass or an increment in weight or height of the organs of the plant influenced by physiological processes.Many of these processes have their limits genetically determined,but climate and irrigation play an important role.Because of its importance,microclimate has been extensively studied in the modeling as a surrounding condition which is imposed by the exterior climate.The main objective of this work was to develop a temperature model based on the energy balance dynamics at two different greenhouse locations-South-eastern Spain and Northern China,and the traditional structures of Chinese solar greenhouse and Almería-type multi-span greenhouse were taken into account.The final model was developed by combining the external conditions,the actuator influence and the crop growth,where the temperature is influenced by soil,crop,cover,actuators,back wall and greenhouse geometry.The model took into account the energy lost by convective and conductive fluxes,as well as the energy supplied by solar radiation and heating systems.The soil and the back wall are the main media for energy storage.The temperature dynamic was determined by a physical model,which considered the energy balance from a holistic point of view-as a sub-model for a customizable interface among the external climate,the plant and the greenhouse system.The influences of different subsystems included in the temperature model were analyzed and evaluated.The results showed a high R^(2)value of 0.94 for Beijing and 0.95 for Almeria,and the average error was low,of which the MAE and RMSE were 0.71 and 1.365 for Almeria and 0.62 and 1.102 for Beijing,respectively.Thus,the model can be considered as a powerful tool for control design purposes in microclimate systems.展开更多
文摘With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.
基金developed within the framework of the Project IoF2020-Internet of Food and Farm 2020,funded by the Horizon 2020 Framework Programme of the European Union,Grant Agreement no.731884,by the Spanish Ministry of Science and Innovation as well as from EUERDF funds under grant DPI2014-56364-C2-1-R,by TEAP project supported by the Marie Curie Actions(PIRSES-GA-2013-612659),by National Natural Science Foundation of China(31401683)by Climate Change Special Founding(CCSF201521)China Meteorological Administration,and by International Cooperation Funding of Beijing Academy of Agricultural and Forestry Sciences(GJHZ2013-4).
文摘Growth can be defined as an increment in biomass or an increment in weight or height of the organs of the plant influenced by physiological processes.Many of these processes have their limits genetically determined,but climate and irrigation play an important role.Because of its importance,microclimate has been extensively studied in the modeling as a surrounding condition which is imposed by the exterior climate.The main objective of this work was to develop a temperature model based on the energy balance dynamics at two different greenhouse locations-South-eastern Spain and Northern China,and the traditional structures of Chinese solar greenhouse and Almería-type multi-span greenhouse were taken into account.The final model was developed by combining the external conditions,the actuator influence and the crop growth,where the temperature is influenced by soil,crop,cover,actuators,back wall and greenhouse geometry.The model took into account the energy lost by convective and conductive fluxes,as well as the energy supplied by solar radiation and heating systems.The soil and the back wall are the main media for energy storage.The temperature dynamic was determined by a physical model,which considered the energy balance from a holistic point of view-as a sub-model for a customizable interface among the external climate,the plant and the greenhouse system.The influences of different subsystems included in the temperature model were analyzed and evaluated.The results showed a high R^(2)value of 0.94 for Beijing and 0.95 for Almeria,and the average error was low,of which the MAE and RMSE were 0.71 and 1.365 for Almeria and 0.62 and 1.102 for Beijing,respectively.Thus,the model can be considered as a powerful tool for control design purposes in microclimate systems.
